BEGIN:VCALENDAR VERSION:2.0 CALSCALE:GREGORIAN PRODID:adamgibbons/ics METHOD:PUBLISH X-PUBLISHED-TTL:PT1H BEGIN:VEVENT UID:1750@https://flypost.ie SUMMARY:Learning to Parrot – Workshop with Dennis McNulty – 30 September 6- 8pm DTSTAMP:20260916T155359Z DTSTART:20260930T170000Z DTEND:20260930T190000Z DESCRIPTION:FSAS is delighted to host this workshop as the first event in a series\nconsidering questions about our institutional relationship to dig ital\ntechnologies and the corporations they tie us to.\n\nLearning to Par rot Workshop with Dennis McNulty\, 6pm – 8pm\, Wednesday 30\nSeptember. Th is event is free but booking is essential. \n[https://www.eventbrite.ie/e/ learning-to-parrot-tickets-2001069122613?aff=oddtdtcreator&keep_tld=true]\ n\nWe are living through an era where artificial intelligence (AI) is surf acing in\nall aspects of people’s lives. These systems are presented as al l-knowing and\nfar too complex for the average person to understand. Learn ing to Parrot is a\ntwo-hour workshop that stages a hands-on encounter wit h the assumptions\nunderlying chatbot technologies such as ChatGPT. The ai m is to demystify them\nand create a space for reflecting on the implicati ons of their use. The title is\na reference to the term Stochastic Parrot and the work of Dr. Emily M. Bender.\n\nPractical Details\n\nParticipants will need to bring a laptop that can connect to the internet and be\nfamil iar with using it. The workshop will last about two hours.\n\nBackground\n \nArtificial intelligence (AI) is widely understood to be a marketing term rather\nthan a description of a coherent set of technologies. That said\, when\nnon-experts refer to AI these days\, they are generally talking abo ut\nchatbot-enabled text generation systems such as OpenAI’s ChatGPT or An thropic’s\nClaude. These kinds of AI products are all based on a technolog y called Large\nLanguage Models (LLMs). As the name might imply\, a langua ge model pulls language\napart into fragments so that computers can proces s it. In order to do that\, the\npeople who created the language model nee d to make some assumptions about what\nlanguage is and how it “works”. As you might imagine\, these assumptions have a\nprofound effect on the langu age that LLM-powered chatbots tend to extrude.\n\nHuman writing emerged fr om a desire to capture knowledge and convey meaning.\nOver time\, the proc ess of writing and reading text has evolved into a rich and\ncomplex suppo rt for communicating\, storing and generating ideas. LLMs are\ndesigned to circumvent any requirement to interpret the meaning of a text and\ninstea d\, leverage the fact that writing takes the form of a sequence of graphic \nsymbols. Like old-school SMS autocomplete\, the language models are cons tructed\nand fed data so that they can predict what the next word in any g iven sequence\nof words is likely be. Chatbot designers believe that meani ng will just emerge\nas a byproduct of generating a sequence of words in a plausible order.\n\nThis approach is described as statistical\, in that w ord prediction is based on\ncalculations of probability rather than any co mmunicative intent. Consider\nwhether you would be more likely to believe in the predictive power of a\npolitical poll based on the opinions of 10 o r 10\,000 voters? This statistical\napproach is the reason why the languag e models grew large. The engineers\nbelieved that analysing more text was the key to building models that could make\nbetter predictions about which word should come next.\n\nIn this exercise we will work through a very ba sic example with a short text to\ndevelop a sense of the limitations assoc iated with modelling language as a\nsequence of words\, particularly with respect to the implications this approach\nmight have for the idea of mean ing.\n\nThis event is free but booking is essential. Book here.\n[https:// www.eventbrite.ie/e/learning-to-parrot-tickets-2001069122613?aff=oddtdtcre ator&keep_tld=true]\n\nAbout Dennis McNulty\n\nDennis McNulty is an artist \, researcher\, music-maker\, and instrument designer\nwhose work grapples with know-ability and is often framed with respect to\ntechnologies such as language\, diagrams or buildings. Learning to Parrot was\ndeveloped in his role as a research assistant in Prof. Dan Kilper’s group at\nCONNECT b ased in Trinity College Dublin\, where he explores the role of diagrams\ni n cross-disciplinary collaboration\, particularly in quantum networking\nr esearch.\n\nHis artwork has been presented at the São Paulo Bienal\, Liver pool Biennial\,\nPerforma Biennial\, IMMA\, Visual\, The Dock and Grazer K unstverein among others.\nMcNulty is currently working on a collaborative Fingal County Council commission\nto mark EU Ireland’s presidency in 2026. www.dennismcnulty.com\n[http://www.dennismcnulty.com] URL:https://flypost.ie/event/learning-to-parrot-workshop-with-dennis-mcnult y-30-september-6-8pm LOCATION:Fire Station Artists' Studios - 9-12 Buckingham Street Lower\, Dub lin 1\, D01 R6P3 STATUS:CONFIRMED CATEGORIES: X-ALT-DESC;FMTTYPE=text/html:

FSAS is delighted to host this work shop as the first event in a series considering questions about our instit utional relationship to digital technologies and the corporations they tie us to.

Learning to Parrot Workshop with D ennis McNulty, 6pm – 8pm, Wednesday 30 September. This event is free but < /strong>b ooking is essential. 

We are living through an era where artificial intelligence (AI) is surfacing in all aspects of people’ s lives. These systems are presented as all-knowing and far too complex fo r the average person to understand. Learning to Parrot is a two-hour works hop that stages a hands-on encounter with the assumptions underlying chatb ot technologies such as ChatGPT. The aim is to demystify them and create a space for reflecting on the implications of their use. The title is a ref erence to the term Stochastic Parrot and the work of Dr. Emily M. Bender.< /p>

Practical Details

Participants will need to b ring a laptop that can connect to the internet and be familiar with using it. The workshop will last about two hours.

Background

Artificial intelligence (AI) is widely understood to be a market ing term rather than a description of a coherent set of technologies. That said, when non-experts refer to AI these days, they are generally talking about chatbot-enabled text generation systems such as OpenAI’s ChatGPT or Anthropic’s Claude. These kinds of AI products are all based on a technol ogy called Large Language Models (LLMs). As the name might imply, a langua ge model pulls language apart into fragments so that computers can process it. In order to do that, the people who created the language model need t o make some assumptions about what language is and how it “works”. As you might imagine, these assumptions have a profound effect on the language th at LLM-powered chatbots tend to extrude.

Human writing emerged from a desire to capture knowledge and convey meaning. Over time, the process o f writing and reading text has evolved into a rich and complex support for communicating, storing and generating ideas. LLMs are designed to circumv ent any requirement to interpret the meaning of a text and instead, levera ge the fact that writing takes the form of a sequence of graphic symbols. Like old-school SMS autocomplete, the language models are constructed and fed data so that they can predict what the next word in any given sequence of words is likely be. Chatbot designers believe that meaning will just e merge as a byproduct of generating a sequence of words in a plausible orde r.

This approach is described as statistical, in that word predictio n is based on calculations of probability rather than any communicative in tent. Consider whether you would be more likely to believe in the predicti ve power of a political poll based on the opinions of 10 or 10,000 voters? This statistical approach is the reason why the language models grew larg e. The engineers believed that analysing more text was the key to building models that could make better predictions about which word should come ne xt.

In this exercise we will work through a very basic example with a short text to develop a sense of the limitations associated with modelli ng language as a sequence of words, particularly with respect to the impli cations this approach might have for the idea of meaning.

This event is free but booking is essential. Book here.

About Denn is McNulty

Dennis McNulty is an artist, researcher, music-m aker, and instrument designer whose work grapples with know-ability and is often framed with respect to technologies such as language, diagrams or b uildings. Learning to Parrot was developed in his role as a research assis tant in Prof. Dan Kilper’s group at CONNECT based in Trinity College Dubli n, where he explores the role of diagrams in cross-disciplinary collaborat ion, particularly in quantum networking research.

His artwork has be en presented at the São Paulo Bienal, Liverpool Biennial, Performa Biennia l, IMMA, Visual, The Dock and Grazer Kunstverein among others. McNulty is currently working on a collaborative Fingal County Council commission to m ark EU Ireland’s presidency in 2026. www.dennismcnulty.com

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